Variable Importance Plots—An Introduction to the vip Package

Abstract:

In the era of “big data”, it is becoming more of a challenge to not only build state-of-the-art by Brandon M. Greenwell, Bradley C. Boehmke Introduction to the vip Package Variable Importance Plots—An

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Published

Sept. 9, 2020

Received

Sep 25, 2019

DOI

10.32614/RJ-2020-013

Volume

Pages

12/1

343 - 366

CRAN packages used

iml, R6, foreach, ingredients, DALEX, mmpf, varImp, party, measures, vita, rfVarImpOOB, randomForestExplainer, tree.interpreter, pkgsearch, caret, mlr, ranger, vip, ggplot2, partykit, earth, nnet, vivo, pdp, microbenchmark, iBreakDown, fastshap, xgboost, ALEPlot, DT, mlr3, data.table, AmesHousing, SuperLearner, glmnet, kernlab, plyr, doParallel

CRAN Task Views implied by cited packages

MachineLearning, HighPerformanceComputing, Multivariate, Survival, Environmetrics, TeachingStatistics, Cluster, Econometrics, Finance, Graphics, ModelDeployment, NaturalLanguageProcessing, Optimization, Phylogenetics, ReproducibleResearch, SocialSciences, TimeSeries

Footnotes

    Reuse

    Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

    Citation

    For attribution, please cite this work as

    Greenwell & Boehmke, "The R Journal: Variable Importance Plots—An Introduction to the vip Package", The R Journal, 2020

    BibTeX citation

    @article{RJ-2020-013,
      author = {Greenwell, Brandon M. and Boehmke, Bradley C.},
      title = {The R Journal: Variable Importance Plots—An Introduction to the vip Package},
      journal = {The R Journal},
      year = {2020},
      note = {https://doi.org/10.32614/RJ-2020-013},
      doi = {10.32614/RJ-2020-013},
      volume = {12},
      issue = {1},
      issn = {2073-4859},
      pages = {343-366}
    }